Model transformation (MT) is a key technology in the model-driven development approach of software engineering that provides automated means to capture the evolution of models and mappings between modeling languages. The pattern and rule-based paradigm of graph transformation is considered a very popular approach for specifying such model transformations. While the expressiveness of different MT specification techniques is frequently compared on well-known transformation problems (e.g. UML-to-XMI, or UML-to-EJB mappings), no such benchmarks exist currently for comparing the performance of different model transformation tools. In the paper, we propose a systematic method for quantitative benchmarking in order to assess the performance of graph transformation tools. Typical features of the graph transformation paradigm and various optimization strategies exploited in different tools are identified and categorized. Moreover, the performance of several popular graph transformation tools is measured and compared on a well-known distributed mutual exclusion problem.